检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

1,856 篇 会议
6 册 图书
1 篇 期刊文献

馆藏范围

1,863 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,364 篇 工学
- 1,248 篇 计算机科学与技术...
- 997 篇 软件工程
- 318 篇 生物工程
- 309 篇 信息与通信工程
- 189 篇 生物医学工程（可授...
- 160 篇 控制科学与工程
- 124 篇 电气工程
- 118 篇 光学工程
- 83 篇 机械工程
- 74 篇 化学工程与技术
- 64 篇 安全科学与工程
- 62 篇 网络空间安全
- 53 篇 电子科学与技术（可...
- 48 篇 仪器科学与技术
747 篇 理学
- 390 篇 数学
- 326 篇 生物学
- 166 篇 统计学（可授理学、...
- 150 篇 物理学
- 134 篇 系统科学
- 83 篇 化学
440 篇 管理学
- 250 篇 管理科学与工程(可...
- 224 篇 图书情报与档案管...
- 128 篇 工商管理
174 篇 医学
- 165 篇 临床医学
- 158 篇 基础医学(可授医学...
- 117 篇 药学(可授医学、理...
- 62 篇 公共卫生与预防医...
78 篇 教育学
- 78 篇 教育学
75 篇 法学
- 68 篇 社会学
50 篇 农学
- 49 篇 作物学
45 篇 经济学
4 篇 军事学

主题

326 篇 machine learning
128 篇 deep learning
77 篇 support vector m...
66 篇 machine learning...
55 篇 forecasting
46 篇 feature extracti...
37 篇 decision trees
36 篇 accuracy
35 篇 computational mo...
34 篇 computer vision
31 篇 predictive model...
30 篇 training
28 篇 data mining
28 篇 students
28 篇 convolutional ne...
26 篇 neural networks
22 篇 object detection
22 篇 artificial intel...
21 篇 sentiment analys...
20 篇 internet of thin...

机构

12 篇 chitkara univers...
7 篇 shenyang ligong ...
6 篇 uttaranchal inst...
5 篇 department of co...
5 篇 maharishi school...
5 篇 alibaba cloud bi...
4 篇 school of comput...
4 篇 college of compu...
4 篇 key laboratory o...
4 篇 lovely professio...
4 篇 jilin agricultur...
4 篇 uttaranchal univ...
4 篇 key laboratory o...
4 篇 department of co...
4 篇 master of comput...
4 篇 school of inform...
3 篇 school of comput...
3 篇 vivekananda glob...
3 篇 department of el...
3 篇 college of compu...

作者

4 篇 liu gang
4 篇 kumar rakesh
4 篇 waseemullah
4 篇 liu peng
3 篇 zhang tao
3 篇 kumar ajay
3 篇 rakesh kumar
3 篇 babu tina
3 篇 siddhant
3 篇 sharma arun
3 篇 faisal rehman
3 篇 wang xiaofei
3 篇 sharif hanan
3 篇 singh sunil kuma...
3 篇 rehman faisal
3 篇 tina babu
3 篇 yang wei
3 篇 sharma avinash
3 篇 prajwal
3 篇 vinay kukreja

语言

1,803 篇 英文
58 篇 其他
16 篇 中文

检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是1711-1720 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Prediction and Modeling of Desalination Performance of Nanofiltration Membranes Based on machine learning

Prediction and Modeling of Desalination Performance of Nanof...

引用

international conference on Artificial Intelligence and computer Information Technology (AICIT)

作者： BoWen Zhang Xiaojian Xu ZengZeng Zhang Lei Yao School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

In the process of desalination, the optimization of nanofiltration membrane is very important to improve the removal rate of sodium chloride. The traditional method is often unstable when the operating conditions change, and it is difficult to adapt to different water quality, which limits its application in desalination processes. In this paper, particle swarm optimization algorithm is used to optimize the backpropagation artificial neural network, and the desalting performance of nanofiltration membrane is predicted and modeled successfully. The accuracy of prediction in the test stage reached 99.38%. This model is conducive to allocating the energy, resources and time of the experiment more rationally. It provides a valuable reference for improving the desalination efficiency of nanofiltration membrane in industry. In addition, this study provides a new perspective on water treatment technology in resource-limited areas, which helps to improve the sustainable use of water resources and the quality of life of residents.

关键词： machine learning algorithms Filtration Desalination Water quality machine learning Predictive models Prediction algorithms Particle swarm optimization Information technology Optimization

来源：评论

学校读者我要写书评

暂无评论

Hurricane Tracking Using Multi-GNSS-R and Deep learning

Hurricane Tracking Using Multi-GNSS-R and Deep Learning

引用

international conference on computer applications & Information Security (ICCAIS)

作者： Meshal Alshaye Faisal Alawwad Ibrahim Elshafiey National Center for Communications King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Electrical Engineering Department King Saud University Riyadh Saudi Arabia

ISBN: (数字)9781728142135

ISBN: (纸本)9781728142142

Hurricane Monitoring using GNSS-R is an emerging application in earth remote sensing. Practically, the hurricane is categorized by its maximum win speed which can be measured from the reflected GNSS signals. In order to track the hurricane movement efficiently, a large number of measurements is required. Alternatively, by using deep learning algorithms, useful information from much fewer measurements can be inferred. In this paper, a deep learning-based technique is proposed to track the core of a moving hurricane using limited measurements of a reflected GNSS signals. The proposed technique has achieved a very high accuracy of 96.6% using a CNN model.

关键词： Hurricanes Global navigation satellite system Wind speed machine learning Monitoring Extraterrestrial measurements Sea measurements

来源：评论

学校读者我要写书评

暂无评论

AI-based Diagnostic Tool for Liver Disease using machine learning Algorithms

AI-based Diagnostic Tool for Liver Disease using Machine Lea...

引用

international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Rex Paolo C. Gamara Antipas T. Teologo Romano Q. Neyra Argel A. Bandala Electrical and Electronics Engineering Department FEU Institute of Technology Manila Philippines Senior Director for Engineering Office FEU Institute of Technology Manila Philippines Department of Electronics and Computer Engineering De La Salle University – Manila Manila Philippines

The liver is the human body's largest internal organ. Globally, liver disease is considered the cause of approximately 2 million yearly death – whereas the 11th and 16th worldwide leading causes of death are cirrhosis and liver cancer. In the Philippines, according to the Department of Health (DOH), liver cancer is ranked as the 3rd leading cause of death. In most cases, surgery may be considered a possible cure if detected at an early stage. However, there is no efficient early detection method for liver cancer. In this paper, multiple machine learning methodologies are modeled to provide diagnosis classification of liver disease based on the laboratory parameter readings. Based on the results for all models, the most accurate prediction is made by ANN at 89%, followed by SVM at 79.5%. The results establish that AI-based machine learning approaches may be utilized for assisting medical-related diagnosis.

关键词： Support vector machines Liver cancer machine learning algorithms Liver diseases Surgery machine learning learning (artificial intelligence)

来源：评论

学校读者我要写书评

暂无评论

computer Aided Image Recognition Technology for Food Microbial Detection

Computer Aided Image Recognition Technology for Food Microbi...

引用

Data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Yanchun Ruan Jiangsu Vocational College Tourism Yangzhou China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

This article aims to explore the application potential of computer-aided image recognition technology in the field of food microbial detection, in order to overcome the limitations of traditional detection methods, such as long time-consuming and cumbersome operation. In order to achieve this goal, an image recognition system of food microorganisms based on advanced image processing algorithm and machine learning model is designed and constructed. In terms of methods, this article uses high-resolution microscope and digital camera to collect images, and uses image preprocessing, segmentation, feature extraction and other technologies to process images; Then, DL (Deep learning) model is applied to learn and classify the extracted microbial characteristics. The experimental results show that the recognition accuracy of the system for Escherichia coli, Salmonella and other food microorganisms is over 95%, and the detection time is significantly shorter than that of the decision tree method. This achievement proves the effectiveness and practicability of computer-aided image recognition technology in food microbial detection. It has a certain contribution to improving the detection ability of food microorganisms.

关键词： Technological innovation Salmonella Image recognition Microorganisms machine learning algorithms Accuracy Computational modeling Feature extraction Food safety Usability

来源：评论

学校读者我要写书评

暂无评论

machine learning Based Computational Electromagnetic Methods for Intelligence CAD/CAE application

Machine Learning Based Computational Electromagnetic Methods...

引用

international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Maxim A. Dubovitskiy Soltanbek Yu. Pashaev Aleksandr O. Zhukov JSC “Special Research Bureau of Moscow Power Engineering Institute” (JSC “OKB MEI”) Moscow Russia National Research University “Moscow Power Engineering Institute” Moscow Russia

ISBN: (数字)9781728183985

ISBN: (纸本)9781728183992

The key issues of combined electrodynamic methods construction in relation to the analysis of antenna systems and microwave elements are considered. Based on combined methods, the general algorithms of the analysis and functioning of modern CAD in the field of electrodynamic modeling are constructed. Further development of computer technologies shows trends towards the creation of intelligent CAD/CAE systems using machine learning to automate computing processes. In this paper, we propose to consider three levels of intelligence achieved in the development of CAD entirely on the organizational principles of Artificial Intelligence systems using both formal and heuristic design procedures.

关键词： Solid modeling Electrodynamics Analytical models Computational modeling machine learning Mathematical model Integrated circuit modeling

来源：评论

学校读者我要写书评

暂无评论

Electricity Demand Forecasting In Kerala Using machine learning Models

Electricity Demand Forecasting In Kerala Using Machine Learn...

引用

Intelligent Technologies (CONIT), international conference on

作者： Anna Saji Arya Prakash M Soumya Krishnan Department of Computer Science & IT School of Computing Amrita Vishwa Vidyapeetham India

In the contemporary era, energy has evolved into a necessity for day-to-day life. Forecasting the demand for electricity is an extremely challenging undertaking. Forecasting the electricity accurately helps to run the power systems efficiently and *** this work, we have developed an electricity demand forecasting model that predicts electricity demand using our own dataset. Our dataset contains 10 years of data (January 2013 to December 2022).The electricity demand data was taken from the Kerala State Electricity Board (KSEB). Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), XGBoost, Artificial Neural Network (ANN) based on machine learning (ML) methods have been applied to observe how these algorithms perform in forecasting electricity. The performance of the discussed ML methods has been analysed based on various evaluation metrics such as accuracy, MAE, MSE, and RMSE. The outcomes obtained from the analysis show that the Random Forest ML approach outperforms the other conventional ML approaches in terms of accuracy, MAE, MSE, and RMSE since it has the highest accuracy (82.72%) and the lowest MAE, MSE, and RMSE score (0.038, 0.002, and 0.054, respectively) compared to the other discussed conventional ML approaches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on Cross-project Defect Prediction Based on Instance Migration Method

Research on Cross-project Defect Prediction Based on Instanc...

引用

computer Science and Management Technology (ICCSMT), international conference on

作者： Wennan Wang Yu Li Shiyang Song Jiadong Lu Baiyu Chen Baoping Wang Academy of Management Guangdong University of Science and Technology Dongguan China Zhuhai Yingying Technology Co. Ltd Zhuhai China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China School of Foreign Language Education Zhuhai College of Science and Technology

The field of software engineering data mining has become increasingly interested in cross-project defect prediction (CPDP), which uses defect codes from other projects to develop prediction models, addressing the problem of insufficient data in the model construction process. However, the source and target projects usually have large differences in data distribution, which reduces the prediction performance. Based on the learning idea of instance migration, the distribution of target project features is changed to be close to the distribution of source project features, thus improving the performance of cross-project defect prediction. Specifically, the cross-project software defect prediction is investigated by combining software metric features with instance migration methods. First, we analyze the difficulty of class imbalance in the training set and extract features from the instances in the training set. Then the idea of improved AdaBoost is proposed. Finally, the algorithm based on instance migration is proposed and experimented, and the result analysis shows the effectiveness and feasibility of the method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep learning Based Event Detection Using Multilingual Tweets During Disaster

Deep Learning Based Event Detection Using Multilingual Tweet...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： C. Valliyammai D. Manikandan M. Bharathkumaran S. Gopi Agasthia P.S Logeswari Department of Computer Technology Madras Institute of Technology Campus Anna University Chennai India

Concerning the calamity, users typically provide data via social media in their native language, which is difficult to process and understand for others who are unfamiliar with that language. The proposed approach is intended to determine the location during a disaster using multilingual tweets. The study analysis with the tweets, extracted using # 2015 Chennai floods and # heavy rain in Tamil language. Multilingual tweets are trained using an auto-encoder model and then disaster-related tweets are grouped using the K-means clustering method. Integration of natural language processing and machine learning techniques helps to extract the location from the tweets. The trained model results in producing the encoded representation of tweets and the number of clusters is determined by the elbow method using K-mean clustering. IndicNER Fine-tuned model with 93% accuracy is used to identify the location entities of the Tamil Tweets extracted. DisMan application is designed to visualize the disaster location in the geographical map and it helps in quick response with the rescue team to identify the disaster-affected area.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Prediction Framework Based on Iterative Optimization with Bayesian Hyperparameter Tuning

Breast Cancer Prediction Framework Based on Iterative Optimi...

引用

Emerging Smart Technologies and applications (eSmarTA), international conference on

作者： Ayman Alsabry Malek Algabri Amin Mohamed Ahsan Mogeeb A.A. Mosleh Aqeel Abdullah Ahmed Hamzah Ali Qasem Department of Computer Science Sana'a University International University of Technology Twintech Sana'a Yemen Department of Computer Science Sana'a University Sana'a Yemen Department of Computer Science International University of Technology Twintech Sana'a Yemen International University of Technology Twintech Taiz University Taiz Sana'a Yemen

Breast cancer (BC) is a major health concern affecting women worldwide, and early detection is crucial for effective treatment and improved survival rates. In this study, we propose a novel BC prediction framework based on iterative optimization with Bayesian hyperparameter tuning applied to the Wisconsin Diagnostic Breast Cancer (WDBC) and Surveillance, Epidemiology, and End Results (SEER) datasets. Our approach employs various machine learning (ML) algorithms, including tree-based, support vector machine (SVM)-based, K-nearest neighbor (KNN)-based, tree-based ensemble, and artificial neural network (ANN)-based ML models. The results demonstrated that the optimized models generally outperformed their non-optimized counterparts. Notably, the optimized AdaBoost model achieved a remarkable performance with 100% accuracy, precision, recall, and F1-score on the WDBC dataset. The optimized GentleBoost model exhibited a high performance of 95.3% accuracy, 97.4% precision, 93.1% recall, 95.2% F1-score, and 0.99 area under the curve (AUC) on the SEER dataset. These findings highlight the potential of our proposed framework for enhancing BC prediction accuracy and robustness, paving the way for future research and clinical application.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Daily, Weekly and Monthly Rain Forecasting using Deep learning

Daily, Weekly and Monthly Rain Forecasting using Deep Learni...

引用

Innovation in Technology (INOCON), IEEE international conference for

作者： Kunal Gupta Rajeev Kumar Gupta Aniket Gupta Computer Science & Engineering Pandit Deendayal Energy University Gandhinagar

This research paper focuses on the extensive exploration and forecasting of daily rainfall, a critical element in weather prediction and environmental planning. The study integrates two-time series datasets spanning from 2013 to 2022, encompassing essential meteorological parameters. Through Exploratory Data Analysis (EDA) visualizations, non-stationary patterns are identified, leading to the application of Seasonal Autoregressive Integrated Moving Average (SARIMA) for capturing temporal dynamics. The study then introduces deep learning, specifically Long Short-Term Memory (LSTM) networks, to enhance rainfall prediction by capturing intricate temporal dependencies. A comparative analysis combines SARIMA with Rolling Forecast, showcasing adaptability to evolving patterns. In this compares we find that SARIMA higher accuracy with a high R2 value. Both are outperformed by the hybrid Rolling Forecast with SARIMA, underscoring the necessity of balancing accuracy and flexibility. The paper highlights SARIMA’s ability to capture temporal dynamics, acknowledges LSTM’s collaboration with meteorological organizations for improved rain forecasting precision in the face of climate change.

关键词：

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共187页 << < 168 169 170 171 172 173 174 175 176 177 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：